Method of optimal sensor selection and fusion for heat exchanger fouling diagnosis in aerospace systems

ABSTRACT

A system and method that determines whether a heat exchanger within a complex networked system is fouling is provided. The system and method includes training classifiers indicative of a plurality of fouling conditions associated with the heat exchanger and testing the classifiers with optimal sensor data from optimal sensors to determine whether the fouling is being experienced by the heat exchanger.

BACKGROUND OF THE INVENTION

In general, a component of the air management system of an aircraft is aheat exchanger, which ensures proper cooling of the engine bleed air.Typically, aircraft heat exchangers suffer from performance degradationdue to a phenomenon called fouling. Fouling occurs when unwantedaccumulation of external substances, debris, and/or organismscontaminate a surface of heat exchanger fins. Further, complexenvironments, such as aerospace systems, operate under varying altitudeslevels, ambient temperatures, passenger loads, and other uncertaintiesthat make fouling diagnosis very challenging. Moreover, determining thecritical sensors to analyze for fouling diagnosis is difficult due tothe interconnections and interdependencies between different componentsand sensors.

To detect fouling, current methodologies are based on regular physicalinspection or observing off-nominal events in sensor data. These eventsare triggered if sensor data crosses simple thresholds that limitdetection to a binary mechanism. These methodologies are also prone tohigh false alarm rate and missed detections. Furthermore, since the airmanagement system has a large sensor suite, large amount of data aregenerated that make the analysis computationally complex. Further, thecurrent methodologies do not perform multi-nary classification offouling severities, do not present the correct classification rate (CCR)and the false alarm rate, do not address the problem of optimal sensorselection and fusion for fouling diagnosis, and are also verysusceptible to noise in the data.

BRIEF DESCRIPTION OF THE INVENTION

According to one embodiment, a method of determining whether a heatexchanger is fouling comprises training classifiers indicative of aplurality of fouling conditions associated with the heat exchanger, theheat exchanger being within a complex networked system herein the airmanagement system; and testing the classifiers with optimal sensor datafrom optimal sensors to determine whether the fouling is beingexperienced by the heat exchanger.

According to another embodiment, a computer program product comprises acomputer readable storage medium having program instructions fordetermining whether a heat exchanger is fouling embodied therewith. Theprogram instructions are executable by a processor to cause theprocessor to perform a training of classifiers indicative of a pluralityof fouling conditions associated with the heat exchanger, the heatexchanger being within a complex networked system; and a testing of theclassifiers with optimal sensor data from optimal sensors to determinewhether the fouling is being experienced by the heat exchanger.

Additional features and advantages are realized through the techniquesof embodiments herein. Other embodiments and aspects of the inventionare described in detail herein and are considered a part of the claimedinvention. For a better understanding of the invention with theadvantages and the features, refer to the description and to thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofthe invention are apparent from the following detailed description takenin conjunction with the accompanying drawings in which:

FIG. 1 depicts a schematic of an air management system coupled with anenvironmental control system in accordance with an embodiment;

FIG. 2 illustrates a process flow of a diagnosis system in accordancewith an embodiment;

FIG. 3 illustrates a training phase process flow of a diagnosis systemin accordance with an embodiment;

FIG. 4 illustrates a testing phase process flow of a diagnosis system inaccordance with an embodiment; and

FIG. 5 illustrates a processing system configured to execute processflows of the diagnosis system in accordance with an embodiment.

DETAILED DESCRIPTION OF THE INVENTION

A detailed description of one or more embodiments of the disclosedapparatus and method are presented herein by way of exemplification andnot limitation with reference to the Figures.

As indicated above, the current methodologies for detecting foulingprovide limited efficiency and accuracy due to the large amount of dataand limited analysis capabilities. Thus, what is needed is a mechanismthat utilizes algorithms of optimal sensor selection and foulingdiagnosis in conjunction with machine learning tools.

In general, embodiments disclosed herein may include a system, method,and/or computer program product (herein diagnosis system) that performsa heat exchanger fouling diagnosis of a complex networked system.Fouling diagnosis is a detection and identification of a fouling levelof a blocked or congested heat exchanger, which is unable to cool amedium at an optimal efficiency, within the complex networked system.

The complex networked system (e.g., an environmental control system) isa system that includes heat exchanges, air cycle machines, condensers,water extractors, etc., each of which are connected via tubes, pipes,ducts, valves, and the like, to supply the medium (e.g., gases, liquids,fluidized solids, or slurries) at a particular flow rate, pressure,and/or temperature to an outlet (e.g., to chambers of a vehicle, such asa cabin or flight deck of an aircraft). The heat exchanger foulingdiagnosis operates in view of difficulties in managing these components,along with sensors coupled to the complex networked system (e.g., thecurrent methodologies described above have extreme difficulty inisolating a correct sensor to diagnose fouling), and in view of externalfactors, such ambient noise, temperatures, and conditions, that affectthe complex networked system (e.g., flying conditions when the complexnetworked system is on an aircraft).

Turning now to FIG. 1, the complex networked system will now bedescribed with reference to an air management system 100 of an aircraft.That is, the air management system 100 may be viewed as an embodiment ofthe complex networked system. The air management system 100 can be anintegrated thermal control system that consists of two main parallelsub-systems, each of which can be an environmental control sub-systemthat provides temperature, humidity, and pressure controlled air to acabin of the aircraft. For the safety and comfort of occupants, it iscritical to ensure that the air management system 100 operates in ahealthy condition. Further, in absence of an onboard fault diagnosistool, the air management system 100 has to be scheduled for periodicmaintenance that causes unwanted interruption of the aircraft operation(possibly for several days), and incurs huge financial costs.

A diagnosis system can be coupled to, integrated with, or receive datafrom the air management system 100. In this way, the diagnosis systemprovides a reliable heat exchanger fouling diagnosis methodology for theair management system 100 that generates early warnings of fouling andenables cost saving maintenance.

The air management system 100 includes a shell 101, a first heatexchanger 110, a second heat exchanger 120, an air cycle machine 140(that can include a compressor 141, turbines 143, 145, and a fan 147), acondenser 150, and a water extractor 160, each of which are connectedvia tubes, pipes, ducts, valves, and the like, such that air is acceptedat Inlet from a source at an initial flow rate, pressure, andtemperature and provided to Outlet (e.g., cabin, flight deck, etc.) at afinal flow rate, pressure, and temperature. The source may be alow-pressure location of an engine of an aircraft, a high-pressurelocation of the engine of the aircraft; recirculated air from a cabin ofthe aircraft; or combination thereof. In this way, credentials of theair at the Outlet (e.g., the final flow rate, pressure, and temperature)enable the aircraft to receive pressurized and cooled air from the airmanagement system 100.

The shell 101 is an example of a ram chamber of a ram system that usesdynamic air pressure created by the aircraft in motion to increase astatic air pressure inside of the shell.

Heat exchangers (e.g., a first heat exchanger 110 and a second heatexchanger 120) are equipment built for efficient heat transfer from onemedium to another. Examples of heat exchangers include double pipe,shell and tube, plate, plate and shell, adiabatic wheel, plate fin,pillow plate, and fluid heat exchangers. In operation, air forcedthrough the shell 101 is blown across the heat exchangers at a variablecooling airflow to control a temperature of the air inside the heatexchanges. For example, the direction through which the inside air flowsis called the hot-side, while the direction through which the shell airflows is called the cold-side of the heat exchanger. With respect tofouling, debris accumulates on the heat exchangers due to severalfactors, including chemical reactions, corrosion, biologicalmultiplications, and freezing, and obstructs the shell air flow. In viewof a configuration of the heat exchangers 110, 120 of the air managementsystem 100, fouling generally occurs on an intake side of the shell 101of the second heat exchanger 120.

The air cycle machine 140 is a mechanical device that regulates apressure of a medium (e.g., increasing a pressure of the air). Examplesof a compressor include centrifugal, diagonal or mixed-flow, axial-flow,reciprocating, ionic liquid piston, rotary screw, rotary vane, scroll,diaphragm and air bubble compressors.

The compressor 141 is a mechanical device that regulates a pressure ofthe bleed air received from the first heat exchanger 110. The turbines143, 145 are mechanical devices that drive the air cycle machine 140.The fan 147 is a mechanical device that assists in manipulating airthrough the shell 110 (e.g., via push or pull methods) for a variablecooling airflow across the heat exchangers 110, 120. The compressor 141,the turbines 143, 145, and the fan 147 together illustrate, for example,that the air cycle machine 140 may operate as a four-wheel air cyclemachine. Note that while FIG. 1 illustrates an example orientation ofthe air cycle machine 140 and its components with respect to the shell101, the first heat exchanger 110, and the second heat exchanger 120,other orientations may be utilized.

The condenser 150, which are typically heat exchangers as describedabove, is a device or unit used to condense the air from a gaseous stateto a liquid state, typically by cooling the air such that latent heat isgiven up by the air and transferred to a condenser coolant. The waterextractor 160 is a mechanical device that performs a process of takingwater from any source, such as air, either temporarily or permanently.

Valves, while not shown in FIG. 1, are devices that regulate, direct,and/or control a flow of the air by opening, closing, or partiallyobstructing various passageways (e.g., tubes, pipes, . . . etc) of theair management system 100. Valves may be operated by actuators such thatflow rates of the air in any portion of the air management system 100may be regulated to a desired value. For example, a valve at the intakeof the shell 110 enables the intake of ambient air external to theaircraft into the shell 101, such that the ambient air may pass throughthe first and second heat exchangers 110, 120 and cool the air beforeexiting as an exhaust (e.g., the method of intake may be a pull methodby a fan 147 of the air cycle machine 140 or a ram method).

In addition, the air management system 100 includes a plurality ofsensors, as represented by sensors A-H. In general, a sensor is anydevice that detects events or changes in quantities and provides acorresponding output, e.g., as an electrical or optical signal. Examplesof quantities that a sensor can detect include but are not limited tolight, motion, temperature, magnetic fields, gravity, humidity,moisture, vibration, pressure, electrical fields, sound, and otherphysical aspects of an external environment. With respect to the airmanagement system 100, the plurality of sensors detects statusinformation of different components and provides that information to thediagnosis system (e.g., a processing device 500 as further describedbelow with respect to FIG. 5). The plurality of sensors can be one offour types of sensors: a flow sensor, a temperature sensor, pressuresensor and a speed sensor. The flow sensors measure the flow rate of theair entering or exiting a component of the air management system 100.The temperature sensors measure the temperature of the air at thatsensor location. The pressure sensor measures the pressure of the air atthat sensor location. The speed sensors measure the revolutions perminute of a component of the air management system 100.

For example, a first sensor is an inlet pressure sensor A; a secondsensor is an inlet mass flow sensor B; a third sensor is a ram air fanspeed sensor C; a fourth sensor is an air cycle machine speed sensor D;a fifth sensor is a compressor outlet temperature sensor E; a sixthsensor is a second heat exchanger outlet temperature sensor; a seventhsensor is condenser inlet temperature sensor G; and an eighth sensor isan environmental control system outlet temperature sensor H. Further,since these sensors A-H are also used to control the components of theair management system 100 to produce the desired temperature, humidity,and pressure at the Outlet, accurate measurements are needed for thecomfort of the passengers onboard the aircraft.

In view of the air management system 100, the diagnosis system will nowbe described with respect to FIGS. 2-5 and with respect to the detectionof a fouling condition within the second heat exchanger 120 (e.g., theheat exchanger fouling diagnosis of the complex networked system).Turning now to FIG. 2, a process flow 200 to perform a fouling diagnosisof the second heat exchanger 120 via training and testing phases isdepicted. Note that the diagnosis system can utilize a simulation modelto simulate the complex networked system (e.g., the air managementsystem 100) in both the testing phase and the training phase.

The process flow 200 begins at block 205, where the diagnosis systemperforms a training phase to train classifiers indicative of variousfouling conditions. In general, the diagnosis system changes differentparameters (e.g., parametric combinations), including ambienttemperature, ambient noise, occupancy count of the aircraft, etc.,within the air management system 100, along with different foulingconditions of the second heat exchanger 120 by modifying a flowimpedance, to generate sensor data corresponding to the plurality ofsensors. Further, an optimal sensor selection is made from the pluralityof sensors of the air management system 100 and utilized to procureoptimal sensor data from the sensor data. Features, extracted from theoptimal sensor data, are then applied to a set of classifiers thatdetermine a severity of the different fouling conditions. The diagnosissystem utilizes a measure of correlations between the severity of thedifferent fouling conditions and the set of classifiers to identifywhich classifiers are trained to indicate which fouling condition (e.g.,the classifiers have been trained to detect types of fouling of the heatexchanger).

Next, the process flow 200 proceeds to block 210, where the diagnosisperforms a testing phase utilizing the trained classifiers to determinewhether a fouling is present within the air management system 100. Forinstance, a fouling condition is received by the diagnosis system, alongwith parametric combinations, to derive a set of unlabeled sensor data.Then, the optimal sensors selected during the training phase areutilized to assist in extracting optimal sensor data from the set ofunlabeled sensor data. Features, extracted from this optimal sensordata, are then applied to the trained classifiers that determine anenhanced decision. The enhanced decision is an indication as to whetherthe fouling condition received by the diagnosis system is in factpresent in the air management system 100.

Then, the process flow proceeds to block 210, where the diagnosis systemoutputs the enhanced decision with respect to the testing phase of block210.

Examples of the training and testing phases will now be described withrespect to FIGS. 3-4. FIG. 3 illustrates a process flow 300 performed bythe diagnosis system. In general, process flow 300 begins, at block 305,with the diagnosis system generating data for each sensor (e.g., sensorsA-H) in the air management system 100. For instance, the diagnosissystem can utilize the simulation model to generate a time series ofsensor data for on ground conditions before the aircraft takes off. Thisis done because debris usually gets kicked up into the shell 101 due towind on the ground and due to the fan 147 pulling air through the heatexchangers 110, 120. In an embodiment, the sensor data depends on theparameters of ambient temperature, occupant count, and heat exchangerfouling; however, the sensor data is not limited to these parameters andmay include different and/or alternative parameters.

Ambient temperature (TA) is a critical external input parameter thataffects sensor readings. In turn, the sensor readings vary significantlywith the ambient temperature, such that ambient temperature could leadto false diagnosis of heat exchanger fouling. In this embodiment, toincorporate the effect of the ambient temperature, sensor data iscategorized into five different day types: i) extremely cold, ii) cold,iii) medium, iv) hot, and v) extremely hot day types. The temperatureranges for each day type are shown in Table 1. Each day type may furtherbe partitioned into eight uniformly spaced temperature values.

TABLE 1 DAY TYPES Day Type TA (degrees F.) Extremely Cold −30-0  Cold 0-30 Medium 30-60 Hot 60-90 Extremely Hot  90-100

Occupant Count (OCC) also affects sensor readings of the air managementsystem 100 because passengers and crew members produce heat loads in thecabin that affects a desired value of the cabin air temperature andpressure. In this embodiment, a number of occupants is grouped into fourcategories: i) Low Load, ii) Medium Load, iii) Heavy Load, and iv) VeryHeavy Load based on the percentage of occupancy in the cabin. The fourcategories of load types are shown in Table 2. Since occupant count hasrelatively less influence on the sensor readings, only the middle pointof each category is used for data generation.

TABLE 2 PASSENGER LOAD CATEGORIES Load Type OCC Low Load  0%-60% MediumLoad 60%-75% Heavy Load 75%-95% Very Heavy Load  95%-100%

Heat exchanger fouling or fouling of the secondary heat exchanger 120 ismodeled as a function of the flow impedance (ZC) at the cold-side thatincreases and in turn reduces the air flow. Equivalently, this lowersthe overall heat transfer coefficient and thus lowers the heat transferand reduces the efficiency of both heat exchanges 110, 120. In thisembodiment, four fouling classes have been defined based on the flowthrough the cold-side of the secondary heat exchanger as follows: i)Green Class (c0)—the safe region where fouling effect is negligible; ii)Yellow Class (c1)—the region where the influence of fouling isobservable and near-time maintenance has to be scheduled; iii) OrangeClass (c2)—the region where immediate action has to be taken; and iv)Red Class (c3)—the region where the operation of the air managementsystem 100 must be immediately shut down for safety. The simulationmodel is run for different values of flow impedance, a resulting flowthrough the second heat exchanger 120 is observed, and a range ofimpedance values are determine for each of the above classes definedbased on the flow. Table 3 shows the impedance intervals associated witheach class, which are open from the right. Each class may further bepartitioned into eight uniformly spaced flow values for data generation.

TABLE 3 DEFINITION OF FOULING CASES Class Flow ZC (kPa · s²/kg²) Green(c0)  80%-100%    0.00-3.62e−01 Yellow (c1) 60%-80% 3.62e−01-1.09e+00Orange (c2) 40%-60% 1.09e+00-3.62e+00 Red (c3)  0%-40% 3.62e+00-1.21e+04

As noted above, the sensor data depends on the parameters, along withvarious fouling conditions. Thus, for each day type, time series dataare generated for various combinations of the above parameters (e.g.,combinations of OCC and TA) to represent each fouling class.

In one operational example, the simulation model was run for differentcombinations of values of ambient temperature (8) (within each daytype), occupant counts (4), and impedance values (8) (within eachfouling class), resulting in a set consisting of a total number of8×4×8=256 runs of time series data. Furthermore, for each day type,similar data sets are generated for all the fouling classes, thusleading to a total of 4×256=1024 runs of time series data. Subsequently,the above data sets are generated for all five day types. Let Γ={γ₁, . .. γ₁₀₂₄} denote the set of parametric combinations and let t ∈ T={1, . .. L=600} denote the set of discrete time indices.

Then, for each day type, the entire data for each sensor s_(i) ∈ S isarranged in a |Γ|×L matrix Zs_(i), where Zs_(i)(γ, t) denotes the sensorreading at time t for parametric combination γ. Thus, for any givens=s_(i) and γ=γ_(j), Z_(si) is a vectorz_(i)(γj,⋅)=[z_(i)(γ_(j),1),z_(i)(γ_(j),2), . . . z_(i)(γ_(j),L)], whichis the time series data for sensor s_(i) for input condition γ₁ ∈ Γ. Forthe purpose of this operational example, the variations in OCC and thevariations of impedance values within each class are considered asuncertainties. There are several other sources of uncertaintiesassociated with the air management system 100 such as measurement noise,mechanical vibrations, and fluctuations in valve positions, which havebeen considered by adding 25 dB white Gaussian noise to the data. Notethat variations in ambient pressure may also be considered.

Next, at block 310, sensor data is labeled with fouling classinformation and is used for optimal sensor selection for each day type.That is, since a large number and variety of sensors are available inthe air management system 100 at different locations, the underlyingprocesses of data generation, storage, and analysis become cumbersome.Therefore, an optimal sensor selection methodology is utilized to rankthe most relevant sensors in terms of the best classificationperformance for heat exchanger fouling diagnosis.

For example, given the sensor set S={s₁, . . . s_(N)}, with N sensors,and the class set C={c₁ . . . c_(M)}, with M classes, the optimal sensorselection is to select a set U^(★) ⊂ S, where |U^(★)|=n, n<N, thatconsists of sensors with maximum classification accuracy and are rankedaccordingly in decreasing order.

Two commonly used sensor selection methods are: i) the wrapper methodand ii) the filter method. Since the wrapper algorithms rank the sensorsbased on their correct classification rate (CCR), a feature extractorand a classifier have to be designed, trained, and applied to allsensors to compute their CCRs, thus making the whole processcomputationally expensive. Furthermore, the wrapper algorithms cannot begeneralized to any classifier. On the other hand, the filter algorithmsevaluate the performance of each sensor based on an optimization of acost function (e.g., information theoretic measure cost function). Anexample of filter algorithms is the minimum Redundancy Maximum Relevance(mRMR) criteria. The mRMR criterion evaluates and ranks the sensors thatbest describe the classes and simultaneously avoid sensors that provideredundant information by means of the following two conditions: i)maximum relevance and ii) minimum redundancy. The maximum relevance is acriterion that aims to find a value that has the maximum average mutualinformation between its sensors and the random variable. The minimumredundancy is a criterion that aims to find a value that has the minimumaverage mutual information between its sensor pairs. In addition, notethat a filter operation of the embedded algorithm facilitates fastexecution of the first round of data reduction and produces a candidatelist of top ranked sensors. Filter algorithms are computationally lessexpensive and do not depend on the choice of a classifier, but they maynot perform as good as the wrapper algorithms.

Embedded sensor selection algorithms (i.e., embedded wrapper and filteralgorithms) circumvent these difficulties by taking advantage of bothwrapper and the filter algorithms by using a filter to select acandidate list of sensors and then applying a wrapper on this list torank and select the optimal set of sensors. In this way, the embeddedwrapper and filter algorithm is used to tradeoff between the lowcomplexity of filter algorithms and the accuracy of wrapper algorithmsin the optimal sensor set selection procedure. Thus, these embeddedalgorithms are less expensive than wrappers, more accurate than filters,and pertinent to the specified classifier. In one example operation, theembedded wrapper and filter algorithm uses a filter algorithm first toselect a candidate list (CL) of n sensors; subsequently, a wrapperalgorithm (which uses a specific classifier) is deployed to select orrank the optimal set of sensors from the candidate list.

This invention introduces another optimal sensor selection methodologybased on an embodiment of an embedded algorithm utilized by thediagnosis system that may be referred to as an unsupervised embeddedalgorithm. This unsupervised embedded algorithm also has the advantagethat it does not depend on the choice of a classifier and enables fasterexecution with very low computational complexity.

The unsupervised embedded algorithm relies on a filter algorithm (e.g.,the mRMR) to select the candidate list (CL) of n sensors. Then, the dataZ_(si) _(i) corresponding to each sensor s_(i) ∈ CL, which consists ofthe data of all classes, are clustered into M clusters using a K-meansclustering algorithm, where M is equal to the number of fouling classes.If these clusters are defined as {O₁, . . . O_(M)} and if a randomvariable Ξ_(j) that is drawn on the cluster Oj and whose outcome belongsto the set of classes C={c₁, . . . c_(M)}, then subsequently, theentropy H(Ξ_(j)),_(j)=1, . . . M, of the class distribution within eachcluster is computed using equation 1. Then, the weighted entropy for allclusters for a sensor s_(j) is calculated as equation 2.

$\begin{matrix}{{H(X)} = {- {\sum_{i = 1}^{r}{p^{i}{lnp}^{i}}}}} & (1) \\{{H_{s_{i}} = {\sum_{j = 1}^{M}{\frac{o_{j}}{\sum_{j^{i} = 1}^{M}{o_{j}}} \cdot {H\left( \Xi_{j} \right)}}}},{\forall{s_{i} \in {CL}}}} & (2)\end{matrix}$

In view of the above, the sensors are ranked according to the decreasingorder of their entropies such that the sensor that has the lowestentropy is ranked the highest and so on. In this fashion the candidatelist CL is re-ranked and a possible list of top ranked sensors (e.g.,optimal sensors) is selected for further analysis. This process ranksthe sensors in the order such that the sensors that have the leastuncertainty between classes in their data clusters are ranked thehighest, thus facilitating a better classification decision.

Once an optimal sensor set is obtained, different machine learningmethods are applied for analysis of sensor data for fouling diagnosis.These methods consists of the feature extraction, at block 315, and theclassification operations, at block 320.

That is, at block 315, the diagnosis system next performs a featureextraction from each optimal sensor. Feature extractions from sensordata of the optimal sensors for heat exchanger fouling diagnosis may beperformed by a principal component analysis (PCA) and/or a Gaussianmixture model (GMM). PCA is a data reduction method, where a data matrixX of dimension d×m (d>m>0, whose columns are data vectors (e.g., sensordata)) is transformed into a matrix Y of size d×m′, where m′<m. Thecolumns of Y hold the Score Vectors (also known as the principalcomponents). GMM, when considering sensor data z_(i)(γ, ⋅)=[z_(i)(γ, 1),. . . z_(i)(γ, L)], is a statistical model of z_(i)(γ,⋅) represented asa sum of R different Gaussian distributions.

Next, once the features are obtained as the principal components or theparameter set of the GMM, the process proceed to block 320 where thediagnosis system performs classifier training by utilizing the featuresto output trained classifiers. For instance, the diagnosis system canprocess the features by classifiers to make a decision on the heatexchanger fouling severity. The diagnosis can use a k-Nearest Neighbor(k-NN) algorithm as a classification technique.

In one example operation, the k-NN classifier is applied by thediagnosis system on a feature space generated by PCA and GMM for eachsensor in a candidate list CL and each day type. For each observationsequence z_(i)(γ,⋅), the PCA based features (i.e., principal components)are d=30 dimensional vectors in m′=2 dimensional feature space while theGMM based features (i.e. the parameter set of GMM) are 6 dimensionalfeature space for each time series data. Since the PCA based featuresare vectors, the k-NN classifier produces d decisions one for each pointin the vector. Then, a single decision is obtained from these decisionsusing the simple majority rule. In addition, sensor fusion can beperformed to enhance the classification performance of block 320.

Turning now to FIG. 4, the testing phase of the diagnosis system willnow be described with respect to process flow 400. In general, processflow 400 begins, at block 405, with the diagnosis system utilizing thesimulation model to generate unlabeled sensor data for a foulingcondition and combinations of OCC and TA. Then, at block 410, thediagnosis system performs an extraction of optimal sensor data utilizingthe optimal sensors (e.g., determined from block 310 of FIG. 3)

Next, at block 415, the diagnosis system performs a feature extractionfrom the optimal sensor data. In turn, the trained classifiers producedfrom the process flow 300 of FIG. 3 are applied across the extractedfeatures to produce a set of classifier decisions based on individualanalysis of each optimal sensor at block 420.

In block 425, the diagnosis system performs a majority vote fusion onthe set of variables to output an enhanced decision. In this way, theenhanced decision may identify whether the second heat exchanger isexperiencing a fouling condition.

Referring now to FIG. 5, there is shown an embodiment of a processingsystem 500 for implementing the diagnosis system. In this embodiment,the processing system 500 has one or more central processing units(processors) 501 a, 501 b, 501 c, etc. (collectively or genericallyreferred to as processor(s) 501). The processors 501, also referred toas processing circuits, are coupled via a system bus 502 to systemmemory 503 and various other components. The system memory 503 caninclude read only memory (ROM) 504 and random access memory (RAM) 505.The ROM 504 is coupled to system bus 502 and may include a basicinput/output system (BIOS), which controls certain basic functions ofthe processing system 500. RAM is read-write memory coupled to systembus 502 for use by processors 501.

In addition to the above, the processing system 500 may include aninput/output (I/O) adapter 506 and a network adapter 507 coupled to thesystem bus 502. I/O adapter 506 may be a small computer system interface(SCSI) adapter that communicates with a hard disk 508 or any othersimilar component. I/O adapter 506 and hard disk 508 may be collectivelyreferred to herein as mass storage 510. Software 511 for execution onprocessing system 500 may be stored in mass storage 510. The massstorage 510 is an example of a tangible storage medium readable by theprocessors 501, where the software 511 is stored as instructions forexecution by the processors 501 to perform a method, such as the processflows 200, 300, 400 of FIGS. 2-4. Network adapter 507 may interconnectsystem bus 502 with an outside network 512 and enable processing system500 to communicate with other such systems. A screen (e.g., a displaymonitor) 515 may be connected to system bus 502 by display adapter 516,which may include a graphics controller to improve the performance ofgraphics intensive applications and a video controller. In oneembodiment, adapters 506, 507, and 516 may be connected to one or moreI/O buses that are connected to system bus 502 via an intermediate busbridge (not shown). Suitable I/O buses for connecting peripheral devicessuch as hard disk controllers, network adapters, and graphics adapterstypically include common protocols, such as the Peripheral ComponentInterconnect (PCI). Additional input/output devices are shown asconnected to system bus 502 via an interface adapter 520 and the displayadapter 516. A keyboard 521, mouse 522, and speaker 523 can beinterconnected to system bus 502 via interface adapter 520, which mayinclude, for example, a Super I/O chip integrating multiple deviceadapters into a single integrated circuit.

Thus, as configured in FIG. 5, the processing system 500 includesprocessing capability in the form of processors 501, storage capabilityin the form of at least the system memory 503, input capability in theform of at least the keyboard 521, and output capability in the form ofat least the display 515. In one embodiment, a portion of system memory503 and mass storage 510 collectively store an operating system tocoordinate the functions of the various components shown in FIG. 5. Notethat in an embodiment, the processing system 500 may be implemented aspart of air management system 100, whether local to a testingenvironment or onboard an aircraft, in communication with the airmanagement system 100. In another embodiments, the processing system 500may be either external or internal to the air management system 100 aslong as the processing system 500 is communicatively coupled to thesensors A-H of the air management system 100, such that outputs from thesensors A-H are received and processed as inputs by the processingsystem 500.

In view of the above, the technical effects and benefits of embodimentsherein include employing a novel algorithm for selection of the optimalsensor set from a suite of a large number of sensors mounted on the airmanagement system of aircraft. The selected sensors are optimal in thesense that they contain the most critical information for foulingdiagnosis of the heat exchanger. The air management system is a complexnetworked system that contains many interconnected components andheterogeneous sensors, thus fault diagnosis becomes a challenging taskbecause of high dimensionality. Given the complexity of the airmanagement system, optimal sensor selection not only reduces thedimensionality of the data by selecting sensors that providenon-redundant and most critical information, but also enhancing thedecision of the classifier by opting out the irrelevant sensors. Notethat because sensor selection based on engineering common sense is notpossible because of component-to-component interconnections and feedbackcontrols, embodiments described herein is beyond current methodologies.

The technical effects and benefits of embodiments herein also include areal-time data-driven built-in-test (BIT) or a classifier that canperform fouling diagnosis in presence of various uncertainties in theair management system and also different operating conditions, such asambient temperature and occupant count. For example, an aircraftoperates in a very noisy environment and sensor measurements are highlysensitive to different flight conditions, such as ambient temperatureand heat load. Thus, these flight conditions considerably affect thesensor readings and the BIT decision in turn. By embracing all thesesources of uncertainties in the analysis of the simulated data, andtraining multiple classifiers (one for each day type), embodimentsillustrate that the trained classifiers perform robustly and deliverhigh correct classification rate and low false alarms. Further, the BITallows for real-time implementation and onboard processing.

The technical effects and benefits of embodiments herein also includeperforming an information fusion of the optimal sensors to improve theperformance of the overall classifier. Information fusion is a criticalstep to allow for condition based maintenance (CBM). Note the currentmethodologies suffer from high false alarm rates, which results inunnecessary expensive and time consuming inspection and maintenance. Incontrast, by extracting information from optimal heterogeneous sensors,technical effects and benefits of embodiments herein achieves a moreaccurate decision and allows for early detection of fouling becausedifferent sensors react to fouling differently.

The technical effects and benefits of embodiments herein also includeincrease in financial savings provided to any company from the abovebenefits, along with an increase in the passengers comfort due touninterrupted airlines service.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects ofembodiments herein. The computer readable storage medium can be atangible device that can retain and store instructions for use by aninstruction execution device.

The computer readable storage medium may be, for example, but is notlimited to, an electronic storage device, a magnetic storage device, anoptical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofembodiments may be assembler instructions, instruction-set-architecture(ISA) instructions, machine instructions, machine dependentinstructions, microcode, firmware instructions, state-setting data, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++ or the like, and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The computer readable program instructions mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider). In some embodiments, electronic circuitryincluding, for example, programmable logic circuitry, field-programmablegate arrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of embodiments.

Aspects of embodiments are described herein with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems), andcomputer program products according to embodiments of the invention. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerreadable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). In some alternative implementations, thefunctions noted in the block may occur out of the order noted in thefigures. For example, two blocks shown in succession may, in fact, beexecuted substantially concurrently, or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved. It will also be noted that each block of the block diagramsand/or flowchart illustration, and combinations of blocks in the blockdiagrams and/or flowchart illustration, can be implemented by specialpurpose hardware-based systems that perform the specified functions oracts or carry out combinations of special purpose hardware and computerinstructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of onemore other features, integers, steps, operations, element components,and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of embodiments herein has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A processor-implemented method of determiningwhether a heat exchanger is fouling, the processor-implemented methodexecutable by a processor of a diagnosis system in communication with anenvironmental control system, the processor-implemented methodcomprising: a) training, by the processor, classifiers to indicate aplurality of fouling conditions associated with the heat exchanger, theheat exchanger being within the environmental control system byutilizing a model to generate sensor data based on the plurality offouling conditions and parametric combinations of ambient temperatureand occupancy count, wherein the ambient temperature is a temperatureexternal to and affects the operation of the environmental controlsystem, the classifiers being encoded as computer executableinstructions on a tangible computer readable memory that when executedby the processor causes the processor to carry out the computerexecutable instructions to indicate the plurality of fouling conditionsassociated with the heat exchanger, wherein the training of theclassifiers further comprises performing an extraction of features fromeach sensor and processing the features by the classifiers to determinea fouling severity for each of the fouling conditions; and b) testing,by the processor, the classifiers with sensor data from sensors todetermine whether the fouling is being experienced by the heatexchanger.
 2. The processor-implemented method of claim 1, furthercomprises: performing a selection of the sensors from a plurality ofsensors, each of the sensors being associated with at least onecomponent within the environmental control system.
 3. Theprocessor-implemented method of claim 2, wherein the selection of thesensors is executed by an unsupervised embedded algorithm that relies ona filter algorithm to select a candidate list of sensors and thenapplies a K-means clustering algorithm to rank sensors.
 4. Theprocessor-implemented method of claim 1, further comprises: outputting adecision with respect to the testing of the classifiers to determinewhether the fouling is being experienced by the heat exchanger.
 5. Theprocessor-implemented method of claim 1, wherein the environmentalcontrol system is an air management system of an aircraft.